Feasibility of Dedicated Breast Positron Emission Tomography Image Denoising Using a Residual Neural Network.

Objectives: This study aimed to create a deep learning (DL)-based denoising model using a residual neural network (Res-Net) trained to reduce noise in ring-type dedicated breast positron emission tomography (dbPET) images acquired in about half the emission time, and to evaluate the feasibility and the effectiveness of the model in terms of its noise reduction performance and preservation of quantitative values compared to conventional post-image filtering techniques.

Methods: Low-count (LC) and full-count (FC) PET images with acquisition durations of 3 and 7 minutes, respectively, were reconstructed. A Res-Net was trained to create a noise reduction model using fifteen patients' data.

Performance Evaluation of a New Dedicated Breast PET Scanner Using NEMA NU4-2008 Standards.

Unlabelled: The aim of this work was to evaluate the performance characteristics of a newly developed dedicated breast PET scanner, according to National Electrical Manufacturers Association (NEMA) NU 4-2008 standards.

Methods: The dedicated breast PET scanner consists of 4 layers of a 32 × 32 lutetium oxyorthosilicate-based crystal array, a light guide, and a 64-channel position-sensitive photomultiplier tube. The size of a crystal element is 1.

Morphofunctional organization in three patients with unilateral polymicrogyria: combined use of diffusion tensor imaging and functional magnetic resonance imaging.

We examined the fiber organization of the brain in three patients with unilateral polymicrogyria (PMG) using diffusion tensor imaging (DTI) in combination with functional magnetic resonance imaging (fMRI). DTI revealed altered fiber tract architecture in patients with PMG. Long projection fibers, such as the corticospinal tract, were reduced the most, whereas long association fibers were less affected.